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False Negative Sample Detection for Graph Contrastive Learning

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Tsinghua science and Technology 2024年第2期29卷 529-542页

作者： Binbin Zhang Li Wang College of data science Taiyuan University of TechnologyJinzhong 030600China

Recently,self-supervised learning has shown great potential in Graph Neural Networks (GNNs) through contrastive learning,which aims to learn discriminative features for each node without label information. The key to graph contrastive learning is data augmentation. The anchor node regards its augmented samples as positive samples,and the rest of the samples are regarded as negative samples,some of which may be positive samples. We call these mislabeled samples as “false negative” samples,which will seriously affect the final learning effect. Since such semantically similar samples are ubiquitous in the graph,the problem of false negative samples is very significant. To address this issue,the paper proposes a novel model,False negative sample Detection for Graph Contrastive Learning (FD4GCL),which uses attribute and structure-aware to detect false negative samples. Experimental results on seven datasets show that FD4GCL outperforms the state-of-the-art baselines and even exceeds several supervised methods.

关键词： graph representation learning contrastive learning false negative sample detection

STDNet: A Spatio-Temporal Decomposition Neural Network for Multivariate Time Series Forecasting

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Tsinghua science and Technology 2024年第4期29卷 1232-1247页

作者： Zhuolun Jiang Zefei Ning Hao Miao Li Wang College of Computer science and Technology(College of data science) Taiyuan University of TechnologyJinzhong 030600China

Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting.

关键词： time series forecasting multivariate time series spatio-temporal decomposition

Cohomology groups of a new class of Kadison-Singer algebras

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science China Mathematics 2024年第3期67卷 593-606页

作者： Guangyu An Xing Cheng Jun Sheng School of Mathematics and data science Shaanxi University of Science and TechnologyXi’an 710021China

Let N be a maximal discrete nest on an infinite-dimensional separable Hilbert space H,ξ=∑^(∞)_(n=1)en/2n be a separating vector for the commutant N',E_(ξ)be the projection from H onto the subspace[Cξ]spanned by the vectorξ,and Q be the projection from K=H⊕H⊕H onto the closed subspace{(η,η,η)^(T):η∈H}.Suppose that L is the projection lattice generated by the projections(E_(ξ) 0 0 0 0 0 0 0 0),{(E 0 0 0 0 0 0 0 0):E∈N},(I 0 0 0 I 0 0 0 0) and *** show that L is a Kadison-Singer lattice with the trivial ***,we prove that every n-th bounded cohomology group H~n(AlgL,B(K))with coefficients in B(K)is trivial for n≥1.

关键词： Kadison-Singer algebra Kadison-Singer lattice derivation cohomology group

THE EXTREMES OF DEPENDENT CHI-PROCESSES ATTRACTED BY THE BROWN-RESNICK PROCESS

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Acta Mathematica Scientia 2024年第2期44卷 686-701页

作者：孙俊杰谭中权 College of data science Jiaxing UniversityJiaxing314001China

Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized poi... 详细信息

关键词： chi-processes Brown-Resnick process pointwise maxima functional limit theorem

Covariate-Assisted Matrix Completion with Multiple Structural Breaks

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Journal of Systems science & Complexity 2024年第2期37卷 692-728页

作者： MENG Jing FENG Long ZOU Changliang WANG Zhaojun School of Statistics and data science KLMDASRLEBPSand LPMCNankai UniversityTianjin 300071China

In matrix completion,additional covariates often provide valuable information for completing the unobserved entries of a high-dimensional low-rank matrix *** this paper,the authors consider the matrix recovery problem when there are multiple structural breaks in the coefficient matrix β under the column-space-decomposition model A=Xβ+B.A cumulative sum(CUSUM)statistic is constructed based on the penalized estimation of β.Then the CUSUM is incorporated into the Wild Binary Segmentation(WBS)algorithm to consistently estimate the location of ***,a nearly-optimal recovery of A is *** findings are further corroborated via numerical experiments and a real-data application.

关键词： Additional covariates matrix completion multiple structural breaks wild Binary Segmentation

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Physical information-enhanced graph neural network for predicting phase separation

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Chinese Physics B 2024年第7期33卷 278-283页

作者：张亚强王煦文王雅楠郑文 Institute of Public-Safety and Big data College of Computer Science and Technology(College of Data Science)Taiyuan University of TechnologyJinzhong 030600China Shanxi Engineering Research Centre for Intelligent data Assisted Treatment Changzhi Medical CollegeChangzhi 046000China Innovation Academy for Microsatellites of Chinese Academy of sciences Shanghai 200050China

Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here,we present a physical information-enhanced graph neural network(PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt *** results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.

关键词： graph neural network phase separation machine learning dissipative particle dynamics

Computing Persistent Homology by Spanning Trees and Critical Simplices

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Research 2024年第2期2023卷 519-527页

作者： Dinghua Shi Zhifeng Chen Chuang Ma Guanrong Chen Department of Mathematics Cllege of ScienceShanghai UniversityShanghaiChina School of Big data Fuzhou University of International Studies and TradeFuzhouChina Department of data science and Big data Technology School of InternetAnhui UniversityHefeiChina Department of Electrical Engineering City University of Hong KongHong KongChina

Topological data analysis can extract effective information from higher-dimensional *** mathematical basis is persistent *** persistent homology can calculate topological features at different spatiotemporal scales of the dataset,that is,establishing the integrated taxonomic relation among points,lines,and ***,the simplicial network composed of all-order simplices in a simplicial complex is *** the sequence of nested simplicial subnetworks can be regarded as a discrete Morse function from the simplicial network to real values,a method based on the concept of critical simplices can be developed by searching all-order spanning *** this new method,not only the Morse function values with the theoretical minimum number of critical simplices can be obtained,but also the Betti numbers and composition of all-order cavities in the simplicial network can be calculated ***,this method is used to analyze some examples and compared with other methods,showing its effectiveness and feasibility.

关键词： Morse establishing topological

Route selection for opportunity-sensing and prediction of waterlogging

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Frontiers of Computer science 2024年第4期18卷 147-159页

作者： Jingbin WANG Weijie ZHANG Zhiyong YU Fangwan HUANG Weiping ZHU Longbiao CHEN College of Computer and data science Fuzhou UniversityFuzhou 350108China Department of Computer science and Technology Xiamen UniversityXiamen 361005China

Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’daily ***,due to feedback delays or high costs,existing methods make large-scale,fine-grained waterlogging monitoring impossible.A common method is to forecast the city’s global waterlogging status using its partial waterlogging *** method has two challenges:first,existing predictive algorithms are either driven by knowledge or data alone;and second,the partial waterlogging data is not collected selectively,resulting in poor *** overcome the aforementioned challenges,this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus *** framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and *** predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural *** combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming *** selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget *** experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost,high accuracy,wide coverage,and fine granularity.

关键词： waterlogging prediction sparse crowdsensing active learning route selection graph convolutional network

Asymptotic Inference in the Random Coefficient Autoregressive Model with Time-functional Variance Noises

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Acta Mathematicae Applicatae Sinica 2024年第2期40卷 320-346页

作者： En-wen ZHU Zi-wei DENG Han-jun ZHANG Jun CAO Xiao-hui LIU Department of Mathematics and Statistics Changsha University of Science and TechnologyChangsha 410114China School of Mathematics and Computational science Xiangtan UniversityXiangtan 411105China School of Statistics and data science and Key Laboratory of Data Science in Finance and EconomicsJiangxi University of Finance and EconomicsNanchang 330013China

This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV *** first establish the consistency and asymptotic normality of the conditional least squares estimator for the constant *** semiparametric least squares estimator for the variance of the random coefficient and the nonparametric estimator for the variance function are constructed,and their asymptotic results are reported.A simulation study is presented along with an analysis of real data to assess the performance of our method in finite samples.

关键词： random coefficient autoregressive model time-functional variance conditional least squares semiparametric least squares